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MCP DuckDuckResearch: Instant Search & Privacy-First AI - MCP Implementation

MCP DuckDuckResearch: Instant Search & Privacy-First AI

MCP DuckDuckResearch: Your privacy-first AI hub! Instant search, web-to-Markdown/Photo magic. Smarter insights, seamless workflows. Unleash productivity, naturally.

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About MCP DuckDuckResearch

What is MCP DuckDuckResearch: Instant Search & Privacy-First AI?

MCP DuckDuckResearch is a privacy-focused search and content extraction solution built on the Model Context Protocol (MCP) framework. It integrates DuckDuckGo’s search capabilities with advanced browser automation, enabling users to retrieve information rapidly while maintaining strict privacy controls. The tool combines instant search results with seamless content harvesting and screenshot capturing, all managed through an API-driven interface.

How to Use MCP DuckDuckResearch: Instant Search & Privacy-First AI?

Implementing MCP DuckDuckResearch involves three core steps: installation, configuration, and integration. First, clone the repository and install dependencies via npm. Configure the MCP server settings within your development environment, then utilize the provided API endpoints to execute searches, extract content, or generate screenshots programmatically. For example, developers can trigger a search query and receive structured data directly from the tool’s REST interface.

MCP DuckDuckResearch Features

Key Features of MCP DuckDuckResearch: Instant Search & Privacy-First AI?

  • Instant DuckDuckGo Search Integration: Leverage DuckDuckGo’s privacy-respecting search engine to fetch results without tracking user activity.
  • Automated Content Extraction: Extract text, metadata, or specific DOM elements from web pages using Playwright’s browser automation.
  • Dynamic Screenshot Capture: Generate screenshots of web content with adaptive resolution and format optimization.
  • Robust Error Handling: Built-in mechanisms to manage network timeouts, invalid URLs, and malformed responses gracefully.
  • Granular Security Controls: Configure strict filtering options to block unsafe content or limit search scope.

Use Cases of MCP DuckDuckResearch: Instant Search & Privacy-First AI?

Common applications include:

  • Academic research requiring anonymous data gathering without user tracking.
  • Automated testing frameworks for validating web content across different scenarios.
  • Content aggregation tools that prioritize user privacy over conventional search engines.
  • Education platforms needing filtered access to web resources for minors.

MCP DuckDuckResearch FAQ

FAQ from MCP DuckDuckResearch: Instant Search & Privacy-First AI?

Q: How does MCP DuckDuckResearch ensure privacy?
The tool uses DuckDuckGo’s privacy-preserving backend and encrypts all communication channels, ensuring no user data is stored or logged.

Q: Is cross-platform support available?
Yes, the MCP server runs on Node.js and supports Linux, macOS, and Windows environments.

Q: Can I customize search filters?
Absolutely. Developers can adjust query parameters to refine results, such as excluding specific domains or setting language preferences.

Q: What happens if a website blocks automation?
The tool employs anti-bot mitigation strategies, including randomized request intervals and header spoofing, to minimize detection risks.

Content

MCP DuckDuckResearch

An MCP (Model Context Protocol) server that combines DuckDuckGo search capabilities with web page content extraction and screenshot functionality. This server bridges the gap between searching for information and accessing web content programmatically.

Features

  • 🔍 DuckDuckGo Search : Search the web using DuckDuckGo's search engine
  • 📄 Content Extraction : Visit web pages and extract their content as Markdown
  • 📸 Screenshot Capture : Take screenshots of web pages with automatic size optimization
  • Robust Error Handling : Built-in protection against bot detection and content validation
  • 🔒 Safe Search Options : Configurable safe search levels for appropriate content filtering

Installation

# Clone the repository
git clone https://github.com/yourusername/mcp-duckduckresearch.git
cd mcp-duckduckresearch

# Install dependencies
npm install

# Build the project
npm run build

Usage with Cline and Roo Code

Installation for Cline

  1. Build the project first using the installation steps above

  2. Configure the MCP server in your Cline settings:

Edit your Cline MCP settings file at: %APPDATA%\Code\User\globalStorage\rooveterinaryinc.roo-cline\settings\cline_mcp_settings.json

Add the following configuration:

    {
  "mcpServers": {
    "duckduckmcp": {
      "command": "node",
      "args": ["path/to/mcp-duckduckresearch/build/index.js"],
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Replace path/to/mcp-duckduckresearch with the actual path where you cloned this repository.

Available Tools

Once configured, the following tools will be available in Roo Code:

1. search_duckduckgo

Search the web using DuckDuckGo. Example usage in Roo Code:

<use_mcp_tool>
<server_name>duckduckmcp</server_name>
<tool_name>search_duckduckgo</tool_name>
<arguments>
{
  "query": "typescript best practices",
  "options": {
    "region": "zh-cn",
    "safeSearch": "MODERATE",
    "numResults": 10
  }
}
</arguments>
</use_mcp_tool>

2. visit_page

Visit a webpage and extract its content as Markdown:

<use_mcp_tool>
<server_name>duckduckmcp</server_name>
<tool_name>visit_page</tool_name>
<arguments>
{
  "url": "https://example.com",
  "takeScreenshot": false
}
</arguments>
</use_mcp_tool>

3. take_screenshot

Take a screenshot of the currently loaded page:

<use_mcp_tool>
<server_name>duckduckmcp</server_name>
<tool_name>take_screenshot</tool_name>
<arguments>
{}
</arguments>
</use_mcp_tool>

Example Workflow in Roo Code

Here's a complete example of searching for information and visiting a result:

  1. First, search for information:
<use_mcp_tool>
<server_name>duckduckmcp</server_name>
<tool_name>search_duckduckgo</tool_name>
<arguments>
{
  "query": "TypeScript best practices",
  "options": {
    "numResults": 10,
    "safeSearch": "MODERATE"
  }
}
</arguments>
</use_mcp_tool>
  1. Then, visit one of the results:
<use_mcp_tool>
<server_name>duckduckmcp</server_name>
<tool_name>visit_page</tool_name>
<arguments>
{
  "url": "https://example.com/typescript-practices",
  "takeScreenshot": true
}
</arguments>
</use_mcp_tool>

The server will automatically handle:

  • Browser initialization and cleanup
  • Content extraction and conversion to Markdown
  • Screenshot capture and optimization
  • Error handling and retries

Development

Prerequisites

  • Node.js (v18 or higher)
  • npm
  • A system capable of running Chrome/Chromium (for Playwright)

Setup Development Environment

# Install dependencies
npm install

# Start in development mode
npm run dev

# Run tests
npm test

# Run tests with coverage
npm run test:coverage

# Format code
npm run format

# Lint code
npm run lint

Project Structure

mcp-duckduckresearch/
├── src/
│   ├── browser.ts     # Browser management and content extraction
│   ├── search.ts      # DuckDuckGo search implementation
│   ├── types.ts       # Type definitions and schemas
│   ├── utils.ts       # Utility functions
│   └── index.ts       # Main server implementation
├── tests/
│   ├── unit/          # Unit tests
│   └── integration/   # Integration tests
└── package.json       # Project configuration

Testing

The project uses Vitest for testing. Tests are organized into:

  • Unit Tests : Testing individual components and functions
  • Integration Tests : Testing the complete workflow
  • Test Coverage : Aiming for >80% coverage

Run tests with:

# Run all tests
npm test

# Run with coverage
npm run test:coverage

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Credits

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